A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques

<p class="0abstract">Nowadays, Deep learning (DL) is the growing trend towards creating visual representations of human body organs for clinical analysis, medical interventions as well as to diagnose and treat diseases. This paper propose a method for neonatal and pediatric brain tu...

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Autores principales:	Prashantha SJ, H.N. Prakash
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	conventional features, deep learning features, genetic algorithm, feature fusion, classification. Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/9792fc8a42da4726b8a85364b39097df
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spelling	oai:doaj.org-article:9792fc8a42da4726b8a85364b39097df2021-11-16T07:23:28ZA Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques2626-849310.3991/ijoe.v17i11.25193https://doaj.org/article/9792fc8a42da4726b8a85364b39097df2021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-joe/article/view/25193https://doaj.org/toc/2626-8493<p class="0abstract">Nowadays, Deep learning (DL) is the growing trend towards creating visual representations of human body organs for clinical analysis, medical interventions as well as to diagnose and treat diseases. This paper propose a method for neonatal and pediatric brain tumors image analysis and prerequisites a T2- weighted MR images only. The pipeline stages of the proposed work as follows: In the first stage, designed a set of specific feature vectors description for high-level classification task using Conventional and deep learning (DL) Feature Extraction methods. The second stage, select a deep features based on proposed convolutional neural network (CNN) method and conventional subset features are from Genetic Algorithm (GA). The third stage, merge the selected features by adapting fusion technique. Finally, predict the brain image is either normal or abnormal. The results demonstrated that the proposed method obtained accurate classification and revealed its robustness to difference in ages and acquisition protocols. The obtained results shows that based on combined deep learning features (DLF) and conventional features have been significantly improves the classification accuracy of the support vector machines (SVM) classifier up to 97.00%.</p>Prashantha SJH.N. PrakashInternational Association of Online Engineering (IAOE)articleconventional features, deep learning features, genetic algorithm, feature fusion, classification.Computer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 11, Pp 124-140 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	conventional features, deep learning features, genetic algorithm, feature fusion, classification. Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	conventional features, deep learning features, genetic algorithm, feature fusion, classification. Computer applications to medicine. Medical informatics R858-859.7 Prashantha SJ H.N. Prakash A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
description	<p class="0abstract">Nowadays, Deep learning (DL) is the growing trend towards creating visual representations of human body organs for clinical analysis, medical interventions as well as to diagnose and treat diseases. This paper propose a method for neonatal and pediatric brain tumors image analysis and prerequisites a T2- weighted MR images only. The pipeline stages of the proposed work as follows: In the first stage, designed a set of specific feature vectors description for high-level classification task using Conventional and deep learning (DL) Feature Extraction methods. The second stage, select a deep features based on proposed convolutional neural network (CNN) method and conventional subset features are from Genetic Algorithm (GA). The third stage, merge the selected features by adapting fusion technique. Finally, predict the brain image is either normal or abnormal. The results demonstrated that the proposed method obtained accurate classification and revealed its robustness to difference in ages and acquisition protocols. The obtained results shows that based on combined deep learning features (DLF) and conventional features have been significantly improves the classification accuracy of the support vector machines (SVM) classifier up to 97.00%.</p>
format	article
author	Prashantha SJ H.N. Prakash
author_facet	Prashantha SJ H.N. Prakash
author_sort	Prashantha SJ
title	A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
title_short	A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
title_full	A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
title_fullStr	A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
title_full_unstemmed	A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
title_sort	features fusion approach for neonatal and pediatrics brain tumor image analysis using genetic and deep learning techniques
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/9792fc8a42da4726b8a85364b39097df
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A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques

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